Introduction
Humanoid progress is easier to understand when failures are documented with the same care as successful runs. An incident database can show which problems repeat across platforms and which are isolated to one test. The goal is not to shame manufacturers. It is to separate evidence from edited highlight reels and improve engineering learning.
Key facts
- Incidents should be classified by observed event rather than guessed root cause.
- Software version task environment and control mode are essential context.
- Near misses can be useful even when no damage occurs.
Record what was observed first
Start with facts such as fall collision dropped object unexpected motion overheating stop network loss or human intervention. Do not label the cause as AI failure hardware failure or operator error unless evidence supports that conclusion. This keeps the database useful when later information changes the diagnosis.
Capture enough context to compare events
Record robot model date site task control mode payload surface weather or indoor conditions software version and whether the event occurred during research competition factory work or a public presentation. The same fall can mean different things in an aggressive stress test and a normal production shift.
Separate severity from publicity
A dramatic fall with no one nearby may be less serious than a small unexpected arm motion next to a worker. Use severity fields for injury property damage production interruption and potential exposure. Media attention should not decide engineering priority.
Track recovery and corrective action
Record whether the robot stopped safely self recovered required remote help needed repair or returned to service after inspection. When a vendor publishes a fix the database can link the corrective action without rewriting the original event.
Use incidents to build better tests
Repeated patterns can become regression tests. Several network related stops suggest communication fault injection. Repeated finger damage suggests endurance testing. Falls after object pickup may lead to balance tests with changing payload. Incident data becomes valuable when it changes validation practice.
Limitations and missing information
- Public information may be incomplete.
- Videos can hide preceding conditions or later damage.
- An observed event should not be used to infer company wide reliability without broader data.
Conclusion
A transparent incident database can make humanoid coverage more technical and fair. It should preserve what happened what is known and what remains uncertain so readers can follow improvements over time.
Sources and methodology
This guide separates published standards and official technical documents from engineering practice. Draft standards are described as work in progress. Product capability is not treated as verified unless a source supports it.
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